Predictive System for Request Approval
A computer implemented method includes receiving a text-based request from a first entity for approval by a second entity-based compliance with a set of rules, converting the text-based request to create a machine compatible converted input having multiple features, providing the converted input to a trained machine learning model that has been trained based on a training set of historical converted requests by the first entity, and receiving a prediction of approval by the second entity from the trained machine learning model along with a probability that the prediction is correct.
1 . A computer implemented method comprising:
receiving a text-based request from a first entity for approval by a second entity-based compliance with a set of rules;
converting the text-based request to create a machine compatible converted input having multiple features;
providing the converted input to a trained machine learning model that has been trained based on a training set of historical converted requests by the first entity; and
receiving a prediction of approval by the second entity from the trained machine learning model along with a probability that the prediction is correct.
2 . The method of claim 1 wherein converting the text-based request comprises separating punctuation marks from text in the request and treating individual entities as tokens.
3 . The method of claim 2 wherein converting is performed by a natural language processing machine.
4 . The method of claim 1 wherein converting comprises tokenizing the text-based request to create tokens.
5 . The method of claim 4 wherein tokenizing the text-based request includes using inverse document frequency to form a vectorized representation of the tokens.
6 . The method of claim 4 wherein tokenizing the text-based request includes using neural word embeddings to form a dense word vector embedding of the tokens.
7 . The method of claim I wherein the trained machine learning model comprises a classification model.
8 . The method of claim l wherein the trained machine learning model comprises a recurrent or convolutional neural network.
9 . The method of claim 1 and further comprising:
iteratively providing different subsets of the multiple features to the trained machine learning model;
receiving predictions and probabilities for each of the provided different subsets; and
identifying at least one subset correlated with approval of the request.
10 . The method of claim 9 wherein iteratively providing different subsets of the multiple features is performed using n-gram analysis.
11 . A machine-readable storage device having instructions for execution by a processor of a machine to cause the processor to perform operations to perform a method of predicting a disposition of requests, the operations comprising:
receiving a text-based request from a first entity for approval by a second entity-based compliance with a set of rules;
converting the text-based request to create a machine compatible converted input having multiple features;
providing the converted input to a trained machine learning model that has been trained based on a training set of historical converted requests by the first entity; and
receiving a prediction of approval by the second entity from the trained machine learning model along with a probability that the prediction is correct.
12 . The device of claim 11 wherein converting the text-based request comprises separating punctuation marks from text in the request and treating individual entities as tokens and is performed by a natural language processing machine.
13 . The device of claim 11 wherein converting the text-based request includes using inverse document frequency to form a vectorized representation of the tokens or using neural word embeddings to form a dense word vector embedding of the tokens.
14 . The device of claim 11 wherein the trained machine learning model comprises a classification model.
15 . The device of claim 11 wherein the trained machine learning model comprises a recurrent or convolutional neural network.
16 . The device of claim 11 wherein the operations further comprise:
iteratively providing different subsets of the multiple features to the trained machine learning model;
receiving predictions and probabilities for each of the provided different subsets; and
identifying at least one subset correlated with approval of the request.
17 . The device of claim 16 wherein iteratively providing different subsets of the multiple features is performed using n-gram analysis.
18 . A device comprising:
a processor; and
a memory device coupled to the processor and having a program stored thereon for execution by the processor to perform operation to perform a method of predicting a disposition of requests, the operations comprising:
receiving a text-based request from a first entity for approval by a second entity-based compliance with a set of rules;
converting the text-based request to create a machine compatible converted input having multiple features;
providing the converted input to a trained machine learning model that has been trained based on a training set of historical converted requests by the first entity; and
receiving a prediction of approval by the second entity from the trained machine learning model along with a probability that the prediction is correct.
19 . The device of claim 18 wherein converting the text-based request comprises separating punctuation marks from text in the request and treating individual entities as tokens and is performed by a natural language processing machine and wherein converting the text-based request includes using inverse document frequency to form a vectorized representation of the tokens or using neural word embeddings to form a dense word vector embedding of the tokens.
20 . The device of claim 18 wherein the operations further comprise:
iteratively providing different subsets of the multiple features to the trained machine learning model;
receiving predictions and probabilities for each of the provided different subsets; and
identifying at least one subset correlated with approval of the request.